Indeed Job Scraper for Salary and Skills Monitoring

What this pipeline does

Job postings are public market signals. They show which roles companies are hiring for, where salaries are disclosed, which skills are being requested, and whether remote work is still part of the offer.

Indeed Job Scraper collects job titles, company names, ratings, locations, remote flags, salary text, normalized salary ranges, job type, snippets, posted dates, job URLs, benefits, and skills mentioned across supported countries. It is useful for recruiting research, compensation checks, market maps, and hiring trend dashboards.

Best first run

Start with one role and one location. Keep the result cap small enough that you can review the rows.

{
  "searchQuery": "data analyst",
  "location": "Amsterdam",
  "country": "NL",
  "maxJobs": 50,
  "datePosted": "last_7_days",
  "enableAiAnalysis": false
}

Check whether the rows contain title, company_name, location, salary_text, skills_mentioned, and job_url. Salary coverage varies by posting, so treat missing salaries as a market fact, not automatically as a scraper failure.

Job row shape

{
  "job_id": "job_123456",
  "title": "Data Analyst",
  "company_name": "Example Corp",
  "location": "Amsterdam, NL",
  "salary_text": "EUR 55,000 - EUR 70,000",
  "skills_mentioned": "SQL, Python, dashboards"
}

Keep both raw salary text and normalized salary fields when available. Raw text is easier to audit; normalized values are easier to chart.

Salary and skills views

Start with these views:

• Salary ranges by role and location
• Skills mentioned most often by role
• Remote vs on-site share by market
• Companies hiring repeatedly for the same role
• New postings by week

These views are practical because they map to decisions: salary bands, recruiting priorities, content ideas, and market-entry research.

Monitoring cadence

For a one-time market report, one run per role-location pair may be enough. For trend monitoring, run the same query weekly with the same country, location, and date filter. Changing the query too often makes trend lines look precise while quietly changing what they measure.

Deployment notes

Store job URLs and run timestamps with every row. Deduplicate by job ID or URL, then keep a first_seen and last_seen date so dashboards can separate fresh postings from jobs that are simply still open.